Showing 4 results for hazbavi
Volume 7, Issue 3 (Summer 2019)
Abstract
Aims: The present study has used results of the application of Revised Universal Soil Loss Equation (RUSLE) in integrated with the economic cost of soil loss to prioritize sub-watersheds of Selj-Anbar Watershed in Mazandaran Province, northern of Iran.
Materials and Methods: Overlay of five input layers of RUSLE model, viz., rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover and management (C) and support and conservations practices (P) factors has been done in Geographical Information system (GIS) platform for the study watershed. Then, the soil loss and sedimentation cost have assessed using soil nutrient depletion analysis. In this method, monetary value to the depleted nutrients based on the cost of purchasing an equivalent amount of used chemical fertilizer in the watershed was assigned.
Findings: The average soil loss and sediment rates of 4.92 and 1.98 t ha-1, respectively was obtained for the study watershed. In addition, the direct and indirect costs caused by soil loss during the five-year period in the Selj-Anbar Watershed were obtained 4.32×105 and 6.40×105 US$ which was totally equal to 10.98×105 US$. The highest (5.59×104 US$) and lowest (1.16×104 US$) annual cost of soil loss was estimated in the sub-watersheds S1-1-1 and S1-1-2, respectively.
Conclusion: Spatial distribution of soil loss and erosion cost could provide a basis for comprehensive and sustainable watershed management. The sub-watersheds with high soil erosion and cost rates deserve superior priority for implementation of conservation activities.
Volume 8, Issue 4 (Fall 2020)
Abstract
Aims: Aim of the present study is to describe the history and outcomes of the Iranian Conference on Watershed Management Sciences and Engineering (WMSE) from 1973 to 2019.
Instruments & Methods: The archives of 14 WMSE conferences were first collected. Then, important information was derived and analyzed. 25 questionnaires were also analyzed.
Findings: The WMSE conference activities interrupted from late-1970s to early 1990s because of the Iran-Iraq war, Iranian Cultural Revolution and closure of the universities. Then, after 18 years from the 3rd WMSE conference, the Watershed Management Society of Iran (WMSI) decided to continue holding the series of watershed management conferences. According to the analysis of the last 11 conferences, 2794 papers with 5029 authors have been presented. In total, 2635, 2177, and 47 students respectively with PhD, MSc, and BSc students were contributed. In addition, 862 and 238 contributions were respectively made from university and research institute parts. The temporal pattern of number of papers published in the WMSE conference showed a cyclic pattern during 11 conferences which increased one and a half times (i.e., 54%) in seven years from 2008 to 2014, followed by a sharp decline in 2016 (71%; Yasouj City) and 2017 (77%; Malayer City).
Conclusion: Despite a large number of papers presented in the WMSE conferences, knowledge about the watershed governance needs to be improved. It was proved that 48, 32, 16, and 4% of the WMSE contributors respectively anticipated the medium, good, bad, and very bad future for WM state in the country.
Volume 16, Issue 96 (February 2020)
Abstract
In this study, the moisture content of kiwifruit in vacuum dryer was predicted using artificial neural networks (ANN) method. The protein (1, 2, 3 and 4%), lactose (4, 6, 8 and 10%), fat (3 and 6%) and temperature (50, 55, 60 and 65ºC) were considered as the independent input parameters and electrical conductivity of recombined milk as the dependent parameter. Experimental data obtained from electrical conductivity meter, were used for training and testing the network. In order to develop neural network firstly experimental data were randomly divided into three sets of training (70%), validating (15%) and testing model (15%). In order to develop ANN models, we used multilayer perceptron with back propagation with momentum algorithm. MLP models trained as two, three and four layers. The total number of hidden layers and the number of neurons in each hidden layer were chosen by trial and error. The best training algorithm was LM with the least MSE value. The highest coefficient of determination (R2) and lowest mean squared error (MSE) were considered as the criterion for selecting the best network. The network having three layers with a topology of 4-4-1 had the best results in predicting the electrical conductivity of recombined milk. This network has two hidden layers with 8 neurons in the first hidden layer and 5 neurons in the second hidden layer. For this network, R2 and MSE were 0.992 and 0.011, respectively. These results can be used in milk processing factories. The correlation between the predicted and experimental values in the optimal topologies was higher than 99%.
Volume 17, Issue 98 (April 2020)
Abstract
Drying of food products using microwave can be a good replacement to hot air dryers. In this study, Response Surface Methodology (RSM) was used for optimization of the conditions for microwave drying of persimmon slices. The effects of microwave power (300, 500 and 700 W) and slice thickness (3, 5 and 7mm) as independent variables on shrinkage percentage, processing time and total color change of persimmon as dependent variables (responses) were evaluated. All process variables were statistically significant as quadratic regression models for all responses. As microwave power increased, the shrinkage percentage and total color change of persimmon slice increased but processing time decreased. As the thickness of persimmon slice increased, the processing time and total color change of persimmon slice increased but shrinkage percentage decreased. The optimum conditions obtained for minimum shrinkage percentage, processing time and total color change were 3 mm as slice thickness and the microwave power of 312 W. In optimized condition, the shrinkage percentage, processing time and total color change of dried persimmon slices were 72.5 %, 5.97 min and 15.2, respectively.
